{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_meta = './wavegan.meta'\n",
    "n = 1000\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "saver = tf.train.import_meta_graph(f_meta)\n",
    "graph = tf.get_default_graph()\n",
    "\n",
    "conv_input = graph.get_tensor_by_name('G/Reshape:0')\n",
    "output = graph.get_tensor_by_name('G/Tanh:0')\n",
    "\n",
    "x = output[0, :, 0]\n",
    "X = tf.spectral.rfft(x)\n",
    "X_mag = tf.abs(X)\n",
    "X_feat = X_mag\n",
    "\n",
    "_conv_input = np.zeros((1, 16, 1024))\n",
    "for i in xrange(1024):\n",
    "    _conv_input[:, i % 16, i] = 1.\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    _outputs = []\n",
    "    for i in xrange(n):\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        \n",
    "        _outputs.append(sess.run(X_feat, {conv_input: _conv_input}))\n",
    "\n",
    "xfeats = np.array(_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f18d04ec790>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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nvwJppGHz1LkFhu54iFAybyxjOn3r+XE++g/7dKf/D3ce5n1ffk53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\nB1xxMTHBOBt0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18d0754650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log = False\n",
    "out_path = 'wavegan_lin_env.pkl'\n",
    "\n",
    "import cPickle as pickle\n",
    "\n",
    "m = np.mean(xfeats, axis=0)\n",
    "if log:\n",
    "    m = np.log(m + 1e-6)\n",
    "m -= m.min()\n",
    "m /= m.max()\n",
    "\n",
    "with open(out_path, 'wb') as f:\n",
    "    pickle.dump(m, f)\n",
    "\n",
    "%pylab inline\n",
    "\n",
    "plt.figure(figsize=(30, 10))\n",
    "plt.plot(m)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
